Choose Your Interface

Same engine, different workflows. Pick the one that fits how you work.

Desktop

For Claude Desktop users. Structured session continuity with quality awareness built in.

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Terminal

For Claude Code users. Configurable quality thresholds and professional workflow tools.

Learn more →
All products

Your AI doesn't have memory problems.
It was never designed to remember.

Every new session starts from zero. Your context, your decisions, your reasoning flow — gone. Not because something broke. Because that's how transformers work.

The Facts
Every AI model loses quality as conversations grow longer. This is proven across 18 frontier models. see research →
Your AI doesn't know it's degrading. It delivers confident, worse answers with no warning. see research →
Instructions get forgotten mid-session. The longer the conversation, the more gets skipped. see research →

What Nimgir Does

Nimgir makes every session a continuation of the last one — not just the facts, but the flow.

Continuation

Seamless Continuation

Mid-brainstorm, mid-code, mid-plan — the next session picks up your exact train of thought. Same reasoning style, same direction, same voice. Like talking to the same colleague every day instead of briefing a new one.

Persistence

Decisions That Stay Decided

What you locked stays locked. An append-only log carries your conclusions forward so nothing gets relitigated next session.

Awareness

Degradation-Aware Sessions

Research confirms quality drops as context grows, but nobody publishes the threshold. On Desktop, Nimgir manages sessions around a tested safe zone. On Terminal, you discover your own threshold and lock it in.

Continuity

Cross-Interface

Desktop and Terminal, same workspaces. Brainstorm in the browser, build in the terminal, nothing lost between them.

Already using the free version? The full version doesn't just continue — it remembers why.

Pricing

One-time purchase. No subscription. Yours forever.

Desktop
$17
one-time
For Claude Desktop users. Structured continuation with session-managed quality awareness.

Need more control? See the Terminal version →

All products

You already know context is finite.
Now you can engineer around it.

Every token competes for the same fixed attention budget. As sessions grow, quality degrades — silently, confidently, invisibly. You've seen it. Nimgir gives you the architecture to manage it.

What the Research Shows
Effective context windows range from 100 to 2,500 tokens across 11 LLMs tested. Up to 99% of the advertised window is unusable for complex reasoning. Paulsen et al., 2025 — MECW see research →
Replacing all irrelevant tokens with whitespace still degraded performance. Input length itself damages reasoning — not content complexity. Du et al., 2025 see research →
AI models cannot accurately self-assess output quality. Confidence does not drop as performance degrades. You get no signal. Aalto University, 2025 see research →
Observation masking outperforms LLM summarization for agent context management. Dumping raw context into the window makes things worse, not better. JetBrains / TU Munich, NeurIPS 2025 see research →
Anthropic's own research describes context as a finite resource with diminishing marginal returns — a limited attention budget every token depletes. Anthropic, 2025 see research →
All 18 frontier models tested — GPT-4.1, Claude 4, Gemini 2.5 — grow increasingly unreliable as input length increases. Universal, replicated, architectural. Chroma Research, 2025 see research →

Session Infrastructure, Not a Wrapper

An MCP server that manages context as the finite resource it is. No prompt templates. No magic. Architecture.

Handover

Continuation Briefings

Structured handover documents carry reasoning chains, decision context, and exact stop points across sessions. Not summaries — continuation state.

Persistence

Append-Only Decision Log

Locked conclusions persist across sessions without re-derivation. The log is the source of truth — no drift, no relitigating.

Threshold

Configurable Quality Cap

Set your own token threshold based on your workflow. Sessions split before degradation hits — at a boundary you define and test, not a generic default.

Workspace

Isolated Workspaces

Independent context per project. No cross-contamination. Same workspace accessible from Desktop and Terminal — brainstorm in browser, build in CLI.

Already using the free version? The full version doesn't just continue — it remembers why.

Pricing

One-time purchase. No subscription. Yours forever.

Terminal
$99
one-time
For Claude Code users. Includes the full Desktop version, plus configurable quality cap and professional workflow tools.
Terminal Team Bundles
7 seats for the price of 5 — save 29%
$495
10 seats for the price of 7 — save 30%
$693
15 seats for the price of 10 — save 33%
$990

The Research Behind Nimgir

Every claim on this site is backed by published research. No marketing assertions. Here are the sources.

Evidence grades classify each finding's strength: Established means scientific consensus across multiple independent studies. Supported means strong evidence with some debate on scope. Emerging means early evidence that needs further validation.
Established Chroma Research, 2025

Multi-Model Context Degradation Study

Tested 18 frontier models including GPT-4.1, Claude 4, and Gemini 2.5. Every single model's performance grows increasingly unreliable as input length grows. Performance degradation as context length increases is real, universal, and replicated.

Read the research
Supported Anthropic, 2025

Context as a Finite Resource

Anthropic's own research describes context as "a finite resource with diminishing marginal returns" — a limited attention budget that every new token depletes. The maker of Claude acknowledges the architectural limitation their product operates under.

Read the research
Supported Aalto University, Finland

The Reverse Dunning-Kruger Effect in AI

AI models cannot accurately assess their own output quality. As performance degrades, the model's confidence does not decrease proportionally — it continues to deliver degraded output with the same apparent confidence. The user has no signal that quality has dropped.

Read the research
Established JetBrains Research & TU Munich — NeurIPS 2025

The Complexity Trap

Observation masking outperforms LLM summarization for agent context management. Raw file contents and exploration threads should be extracted for decisions and status markers, not fed whole into context. More context does not mean better performance — it often means worse.

Read the research
Emerging Paulsen et al., 2025

Maximum Effective Context Window (MECW)

Across eleven LLMs, the effective context window — the amount of context the model can actually use for complex reasoning — ranges from as low as 100 to 2,500 tokens depending on model and task type. Up to 99% of the advertised context window may be unusable for complex tasks.

Read the research
Established Du et al., 2025

Length Itself Causes Degradation

In a controlled experiment, all non-relevant tokens were replaced with blank spaces — and performance still degraded. This proves that input length itself damages reasoning, not content complexity. The problem is architectural, not solvable by better prompting.

Read the research

Yasal Bilgiler